In the present dynamic world, where decision making process for making highest profit by an individual or a company runs a risk of whirling out to be negative, minimizing the risk by considering consistent entity into decision making process is very vital. Exemplary areas where decision making processes are crucial include investment analysis, betting systems where player need to rely on companies and player/team in terms of future performance, and the like.
Presently there are many forecasting models used on time series data to forecast future performances and based on the predicted values decisions are taken. Besides forecasting approaches, models for detecting streak (consistent pattern in the time series) are also very crucial in finding low risk profitable business. Model/Data Simulation and Bayesian binary segmentation procedures are among the few streak identification approaches derived previously.
Existing model/data simulation techniques are typically based on the Markov switching model, which suggests to first converting the time series into binary data by applying threshold values. The model is parameterized by three numbers: the hot and cold probabilities Pc (probability of hitting a cold state), Ph (Probability of hitting a hot state) and a staying probability a. The model depends upon a Markov switching model to identify streakiness in a given time series and says, an entity switches between the hot and cold hitting states for different instances of time according to a Markov chain.
Bayesian binary segmentation relates to a segmentation procedure for locating the change-points and the associated success rate simultaneously. This procedure is based on a series of nested hypothesis test each using Bayesian factor or the Bayesian Information Criterion (BIC). This model goes on splitting a binary time series based on hypothesis test until no more change points are observed.
Using existing techniques can result in significant limitations in streak prediction including, for example, potential loss of information due to Binary conversion, impractical streak continuity prediction, a limited scope of applications due to Binary data support only, and the impracticality of assuming the presence of disjointed streaks.
In addition, existing streak identification algorithms do not focused on finding overlapping streaks. They either assume the whole time series as one streak or multiple disjointed streaks. But in practical cases, there might be streaks which are overlapping to each other.
There are also known algorithms which are capable of finding streaks from data provided the time series is binary in nature. But conversion of numerical time series to binary by just doing many-to-one mapping techniques using threshold values usually result in huge losses of information.
Furthermore, a streak can be defined as a period of consistent performance. In time series data, there might be certain length of data points where much irregularity is possibly observed, which may not be suitable to fall under a streak. So discontinuity in streaks is a very practical case, which has not been addressed using existing techniques.
In view of the above problems with existing streak identification techniques, there is a clear need to identify streaks in time series data and predict future streaks. The preferred embodiment described herein meets this need.